An Improved PSO Method Based on Adaptive Cognitive Domain
LIU Tun-Dong1, CHEN De-Bao2, LI Su-Wen2, WANG Ying1
1.Department of Automation, Xiamen University, Xiamen 361005 2.Physics and Electronic Information Institute, Huaibei Coal Industry Teachers College, Huaibei 235000
Abstract：To improve the convergent performance of particle swarm optimization(PSO), an adaptive cognitive domain particle swarm optimization(ACDPSO) method is proposed. In the updating equations of particles, the current best position, which the particle achieves, is determined by the center of the best calculated position and the cognizant direction of the particle. Linear decreasing inertia weight is used to optimize particles. Three different PSOs, particle swarm with constant weight(CWPSO), linear decreasing inertia weight PSO(LDWPSO) and Ladder PSO(LPSO), are combined with the proposed method to test the performance of the proposed method, and the results indicate that the proposed method is effective.
刘暾东，陈得宝，李素文，王颖. 基于自适应认知域的粒子群性能改进方法*[J]. 模式识别与人工智能, 2009, 22(5): 726-730.
LIU Tun-Dong, CHEN De-Bao, LI Su-Wen, WANG Ying. An Improved PSO Method Based on Adaptive Cognitive Domain. , 2009, 22(5): 726-730.
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2009, Vol. 22 
